ETH Zürich DLSC: Fourier Neural Operators and Convolutional Neural Operators

↓↓↓ LECTURE OVERVIEW BELOW ↓↓↓
ETH Zürich Deep Learning in Scientific Computing 2023
Lecture 11: Fourier Neural Operators and Convolutional Neural Operators

Lecturers: Ben Moseley and Siddhartha Mishra

▬ Lecture Content ▬▬▬▬▬▬▬▬▬
0:00 - Recap: previous lecture
4:47 - Theoretical properties of Fourier neural operators (FNOs)
9:59 - DeepONets vs FNOs
19:35 - Issues with FNOs
26:20 - Aliasing in FNOs
40:57 - Convolutional neural operators (CNOs)
50:15 - CNO architectures in practice
57:09 - Performance of CNOs
1:13:41 - Open issues in operator learning

▬ Course Overview ▬▬▬▬▬▬▬▬▬

▬ Course Learning Objectives ▬▬▬▬▬
The objective of this course is to introduce students to advanced applications of deep learning in scientific computing. The focus will be on the design and implementation of algorithms as well as on the underlying theory that guarantees reliability of the algorithms. We provide several examples of applications in science and engineering where deep learning based algorithms outperform state of the art methods.

By the end of the course you should be:
- Aware of advanced applications of deep learning in scientific computing
- Familiar with the design, implementation and theory of these algorithms
- Understand the pros/cons of using deep learning
- Understand key scientific machine learning concepts and themes

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